On the second day of the Moore–Sloan Data Science Summit, we did some awesome community building exercises involving team problem-solving. We then discussed and tried to understand how it relates to our ideas about collaboration and creativity. That was pretty fun!
At lunch I had a great conversation with Philip Stark (Berkeley) about finding signals in time series below the Nyquist (sampling) limit; in principle it is possible if you have a good idea what you are looking for or what's hidden there. We also talked about geometric descriptions of statistics: The world is infinite dimensional (there are a set of fields at every position in phase space) but observations are finite (noisy measurements of certain kinds of projections). This has lots of implications for the impact of priors (such as non-negativity), when they apply to the infinite-dimensional object (the latent variables, rather than the finite observations).
After lunch, it was probabilistic generalizations of periodograms with Jake Vanderplas (UW) and some frisbee, and then a discussion about the open spaces for Data Science that we are building at Berkeley, UW, and NYU. In all three, there are issues of setting the rules and culture of the space. I think the three institutions can make progress together that no one institution could make on its own.
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